Abstract

In this thesis we develop computational techniques for modelling molecular selforganisation.
After a short review of the current nanotechnological applications of
molecular self-assembly and the main problems encountered in modelling the selforganised
behaviour of chemical systems, we introduce a set of methods, from both
chemistry and complexity science, for the prediction of self-assembled structures,
with particular focus on Monte Carlo (MC) based methods.
We apply the MC method to two systems of experimental interest. First we
model the silica nanoparticles on the surface of spherical polystyrene latex droplets,
synthesised by the S. Bon Group at the University of Warwick, as a set of soft
spheres on a spherical surface, to study their packing patterns as a function of
the broadening of the nanoparticle size distribution. Then we develop a hexagonal
lattice model for the study of the two-dimensional self-organisation of planar
molecules capable of complementary interactions, to study their phase diagrams as
a function of the strength of their complementary interactions and bonding motif.
In both cases, the phases are characterised using a number of order parameters.
We show that these simplified models are able to reproduce the experimental observations.
We then develop an Agent Based (AB) algorithm, traditionally used for the
study of complex systems, for the modelling of molecular self-organisation. In this
algorithm, an agent is identified with a stable portion of the system under investigation.
The agents can then evolve following a set of rules which include elements
of adaptation (new configurations induce new types of moves) and learning (past
successful choices are repeated), in order to drive the system towards its lowest
energy configuration. We first apply the method to the study of the packing of a
set of idealised shapes, then we extend it to the study of a realistic system. The
latter is achieved by linking the AB algorithm to an available molecular mechanics
code, in order to calculate the interaction energies of atomistic models. In both
cases we compare the AB result with that of MC based methods, showing that
for all the systems studied, the AB method consistently finds significantly lower
energy minima than the MC algorithms in less computing time. Finally, we show
how the AB algorithm can be used as a part of the protocol to calculate the phase
diagram of a rigid organic molecule (1,4-benzene-dicarboxylic acid or TPA) with
less computational effort than standard techniques.